Stem Cell Imaging: Tools to Improve Cell Delivery and Viability.

Stem cell therapy (SCT) has shown very promising preclinical results in a variety of regenerative medicine applications. Nevertheless, the complete utility of this technology remains unrealized. Imaging is a potent tool used in multiple stages of SCT and this review describes the role that imaging plays in cell harvest, cell purification, and cell implantation, as well as a discussion of how imaging can be used to assess outcome in SCT. We close with some perspective on potential growth in the field

Twenty-six circulating antigens and two novel diagnostic candidate molecules identified in the serum of canines with experimental acute toxoplasmosis

List of CAg proteins identified by LC-MS/MS after IP enrichment and purification with ESA antibodies. (XLSX 27 kb

Boosting NIR Laser Marking Efficiency of a Transparent Epoxy Using a Layered Double Hydroxide

Efficient near-infrared (NIR) laser marking on transparent polymers like polypropylene, epoxy, and polyethylene has posed a big challenge due to their lack of absorption in the NIR. Currently, inorganic additives are used to improve NIR laser marking efficiency, but they come with issues such as toxicity, high loading requirement, adverse effects on color/opaqueness, and the need for low laser head speeds. Herein, we report a new strategy of incorporating a food-grade, Mg2Al-CO3 LDH as a boosting coadditive alongside the commercial NIR laser marking additive (Iriotech 8815) in an epoxy system. Our findings demonstrate that the incorporation of Mg2Al-CO3 LDH can significantly increase both the darkness and contrast of marking even at high laser head speed (5000 mm/s), while minimizing surface damage. Notably, by replacing 95% of Iriotech 8815 with Mg2Al-CO3 LDH, an epoxy plate can exhibit high transparency, while producing dark, sharply defined markings with excellent readable QR code markings at high laser speeds. This result offers a promising solution for enhancing high-speed NIR laser marking on transparent polymers with additional advantages of lower toxicity and cost and with minimal optical interference from high additive loadings

Instruction-based learning: a review

Humans are able to learn to implement novel rules from instructions rapidly, which is termed "instruction-based learning" (IBL). This remarkable ability is very important in our daily life in both learning individually or working as a team, and almost every psychology experiment starts with instructing participants. Many recent progresses have been made in IBL research both psychologically and neuroscientifically. In this review, we discuss the role of language in IBL, the importance of the first trial performance in IBL, why IBL should be considered as a goal-directed behavior, intelligence and IBL, cognitive flexibility and IBL, how behaviorally relevant information is processed in the lateral prefrontal cortex (LPFC), how the lateral frontal cortex (LFC) networks work as a functional hierarchy during IBL, and the cortical and subcortical contributions to IBL. Finally, we develop a neural working model for IBL and provide some sensible directions for future research

Optimal suppression strategy for capacitor voltage ripples of hybrid MMCs under unbalanced grid voltages

Submodule (SM) capacitor voltage ripples in modular multilevel converters (MMCs) can be suppressed by harmonic injection strategy, which can reduce the volume and cost of MMCs. The existing methods using the dual harmonic injection strategy for hybrid MMCs only consider steady-state conditions. Harmonic injection strategies for unbalanced grid voltage conditions only use single harmonic injection and can be applied for single-phase faults. To address the above issues, this paper proposes an optimal dual harmonic injection of second-order harmonic current and third-order harmonic voltage strategy for hybrid MMCs. Firstly, the mathematical model of MMC under unbalanced grid voltages is established. Then the ripple characteristics of SM capacitor voltage are analyzed to reveal the relationship between the capacitor voltage and the arm power ripples. Based on the developed instantaneous arm power model, optimal harmonic injection parameters are calculated. A hybrid MMC simulation model is built in PSCAD/EMTDC to verify the effectiveness of the proposed strategy. The results show the excellent performance of the proposed strategy in suppressing the voltage ripples under unbalanced voltage conditions

Exploring causal relationships between inflammatory cytokines and allergic rhinitis, chronic rhinosinusitis, and nasal polyps: a Mendelian randomization study

ObjectivesPrevious research has suggested connections between specific inflammatory cytokines and nasal conditions, including Allergic Rhinitis (AR), Chronic Rhinosinusitis (CRS), and Nasal Polyps (NP). However, a lack of robust research establishing the causal underpinnings of them. This Mendelian Randomization (MR) study aims to evaluate the causal relationships between 41 inflammatory cytokines and the incidence of AR, CRS and NP.MethodsThis study employed a two-sample MR design, harnessing genetic variations derived from publicly accessible genome-wide association studies (GWAS) datasets. AR data was sourced from a GWAS with 25,486 cases and 87,097 controls (identifier: ukb-b-7178). CRS data originated from a GWAS encompassing 1,179 cases and 360,015 controls (identifier: ukb-d-J32). NP data was extracted from a GWAS involving 1,637 cases and 335,562 controls (identifier: ukb-a-541). The data for 41 inflammatory cytokines were obtained from an independent GWAS encompassing 8,293 participants. Inverse variance weighted (IVW), MR Egger regression and Weighted median were used to evaluate the causalities of exposures and outcomes. A range of sensitivity analyses were implemented to assess the robustness of the results.ResultsThe results revealed significant associations between elevated circulating levels of MIP-1α (odds ratio, OR: 1.01798, 95% confidence interval, CI: 1.00217–1.03404, p = 0.02570) and TNF-α (OR: 1.01478, 95% CI: 1.00225–1.02746, p = 0.02067) with an augmented risk of AR in the IVW approach. Heightened levels of circulating IL-2 exhibited a positive correlation with an increased susceptibility to NP in the IVW approach (OR: 1.00129, 95% CI: 1.00017–1.00242, p = 0.02434), whereas elevated levels of circulating PDGF-BB demonstrated a decreased risk of NP (OR: 0.99920, 95% CI: 0.99841–0.99999, p = 0.047610). The MR analysis between levels of 41 inflammatory cytokines and the incidence of CRS yielded no positive outcomes.ConclusionThis investigation proposes a potential causal association between elevated levels of MIP-1α and TNF-α with an elevated risk of AR, as well as an increased risk of NP linked to elevated IL-2 levels. Furthermore, there appears to be a potential association between increased levels of circulating PDGF-BB and a reduced risk of NP

LAG-YOLO: Efficient road damage detector via lightweight attention ghost module

Road damage detection plays an important role in ensuring road safety and improving traffic flow. The dramatic progress of artificial intelligence (AI) technology offers new opportunities for this field. In this paper, we introduce lightweight attention ghost-you only look once (LAG-YOLO), an efficient deep-learning network for road damage detection. LAG-YOLO optimizes the network structure of YOLO, making it more suitable for real-time processing and lightweight deployment while ensuring high accuracy. In addition, a novel module called attention ghost is designed to reduce the model parameters and improve the model performance by the simple attention module (SimAM). LAG-YOLO achieves an impressive parameter reduction to 4.19 million, delivering remarkable mean average precision (mAP) scores of 45.80% on the Hualu dataset and 52.35% on the RDD2020 dataset. In summary, the proposed network performs satisfactorily on extensive road damage datasets with fewer parameters, making it more suitable to be deployed in practice

Deciphering colorectal cancer radioresistance and immune microrenvironment: unraveling the role of EIF5A through single-cell RNA sequencing and machine learning

BackgroundRadiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs and TiME.MethodsPredictive models for preoperative radiotherapy (preRT) response were developed using machine learning, identifying EIF5A as a key gene associated with radioresistance. EIF5A expression was analyzed via bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Functional assays and in vivo experiments validated EIF5A’s role in radioresistance and TiME modulation.ResultsEIF5A was significantly upregulated in radioresistant colorectal cancer (CRC) tissues. EIF5A knockdown in CRC cell lines reduced cell viability, migration, and invasion after radiation, and increased radiation-induced apoptosis. Mechanistically, EIF5A promoted cancer stem cell (CSC) characteristics through the Hedgehog signaling pathway. Analysis of the TiME revealed that the radiation-resistant group had an immune-desert phenotype, characterized by low immune cell infiltration. In vivo experiments showed that EIF5A knockdown led to increased infiltration of CD8+ T cells and M1 macrophages, and decreased M2 macrophages and Tregs following radiation therapy, thereby enhancing the radiotherapy response.ConclusionEIF5A contributes to CRC radioresistance by promoting CSC traits via the Hedgehog pathway and modulating the TiME to an immune-suppressive state. Targeting EIF5A could enhance radiation sensitivity and improve immune responses, offering a potential therapeutic strategy to optimize radiotherapy outcomes in CRC patients

Automatically constructing a health indicator for lithium-ion battery state-of-health estimation via adversarial and compound staked autoencoder

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Precisely assessing the state of health (SOH) has emerged as a critical approach to ensuring the safety and dependability of lithium-ion batteries. One of the primary issues faced by SOH estimate methods is their susceptibility to the influence of noise in the observed variables. Moreover, we prefer to automatically extract explicit features for data-driven methods in certain circumstances. In light of these considerations, this paper proposes an adversarial and compound stacked autoencoder for automatically constructing the SOH estimation health indicator. The compound stacked autoencoder consists of two parts. The first one is a denoising autoencoder that learns three different denoising behaviors. The second is a feature-extracting autoencoder that employs adversarial learning to improve generalization ability. The experimental results show that the proposed compound stacked autoencoder can not only get explainable explicit features but also can achieve accurate SOH estimation results compared with its rivals. In addition, the results with transfer learning demonstrate that the proposed method not only can provide high generalization ability but also be easily transferred to a new SOH estimation task

Self-assembly of gold nanoparticles to carbon nanotubes using a thiol-terminated pyrene as interlinker

Abstract Gold nanoparticles were self-assembled onto the surface of solubilized carbon nanotubes through an interlinker of bi-functionalized molecule (PHT) terminated with pyrenyl unit at one end and thiol group at the other end. While the fluorescence of PHT is quenched moderately by the carbon nanotubes, the fluorescence is almost totally quenched by the further binding of gold nanoparticles. The enhancement of the Raman responses of nanotubes by the gold nanoparticles is also observed. These results imply there are charge transfer interactions between nanotubes and gold nanoparticles